Health · Full coverage
AI Model Could Cut Thousands of Unnecessary Blood Tests in Complex Diagnostics
How 2 Israeli newsrooms covered this story — translated into English and compared side by side.
50% center
Center 1Right 1
First reported by Ynet · 1 hour ago
What happened
Researchers at Ichilov Medical Center developed an AI model that predicts negative lupus anticoagulant test results early, potentially reducing unnecessary blood tests by about 90%. The model was correct 98% of the time when identifying likely negative samples, which could save resources and speed up diagnostics if adopted widely.
- 01AI model predicts negative lupus anticoagulant tests early, reducing unnecessary blood tests.
- 02Study analyzed 7,454 tests, with nearly 90% negative results.
- 03Model used two routine coagulation tests and identified patterns with 98% accuracy.
- 04AI is not a replacement but a tool to avoid further testing in likely negative cases.
- 05Approach could shorten wait times and save lab resources if validated elsewhere.
- 06Research led by Dr. Bentzi Katz at Ichilov and published in Digital Medicine.
Summary translated & synthesized from the sources below by baba. Read each original for the full report.
Full coverage · 2 outlets
The same event, reported separately by each newsroom. Open a few to compare what each emphasizes — and what they leave out.
Related stories
Israeli Health Ministry Tests AI Systems for Autonomous Medical DecisionsJul 9, 2026AI System Helps Doctors Identify 18 Rare Childhood DiseasesJun 21, 2026Israeli Researchers Develop Affordable Blood Test to Detect Lung Cancer EarlyAug 4, 2026Rambam Hospital Deploys AI System to Enhance Emergency Diagnoses and Save LivesJul 23, 2026Israeli Experts Highlight AI’s Role in Early Brain Bleed Detection and Medical InnovationJul 8, 2026New Blood Test Detects Early Alzheimer’s Signs Decades Before Symptoms AppearJul 2, 2026
